Evidence map›Paper›PMID 39156805›Full record

ArticleFrontiers in psychology2024

Hearing temperatures: employing machine learning for elucidating the cross-modal perception of thermal properties through audition.

Mohr Wenger, Amber Maimon, Or Yizhar, Adi Snir, Yonatan Sasson, Amir Amedi

Abstract read
In one paragraph

Article in Frontiers in psychology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Mohr Wenger *Baruch Ivcher Institute for Brain Cognition and Technology, Baruch Ivcher School of Psychology, Reichman University, Herzliya, Israel.
Amber Maimon *Baruch Ivcher Institute for Brain Cognition and Technology, Baruch Ivcher School of Psychology, Reichman University, Herzliya, Israel.
Or YizharBaruch Ivcher Institute for Brain Cognition and Technology, Baruch Ivcher School of Psychology, Reichman University, Herzliya, Israel.
Adi SnirBaruch Ivcher Institute for Brain Cognition and Technology, Baruch Ivcher School of Psychology, Reichman University, Herzliya, Israel.
Yonatan SassonBaruch Ivcher Institute for Brain Cognition and Technology, Baruch Ivcher School of Psychology, Reichman University, Herzliya, Israel.
Amir AmediBaruch Ivcher Institute for Brain Cognition and Technology, Baruch Ivcher School of Psychology, Reichman University, Herzliya, Israel.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

People can use their sense of hearing for discerning thermal properties, though they are for the most part unaware that they can do so. While people unequivocally claim that they cannot perceive the temperature of pouring water through the auditory properties of hearing it being poured, our research further strengthens the understanding that they can. This multimodal ability is implicitly acquired in humans, likely through perceptual learning over the lifetime of exposure to the differences in the physical attributes of pouring water. In this study, we explore people's perception of this intriguing cross modal correspondence, and investigate the psychophysical foundations of this complex ecological mapping by employing machine learning. Our results show that not only can the auditory properties of pouring water be classified by humans in practice, the physical characteristics underlying this phenomenon can also be classified by a pre-trained deep neural network.

Indexed as

cross-modal correspondencesmultimodalmultisensory integrationsensorythermal perception

Identifiers

PMID39156805
PMCPMC11327021

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.